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The interaction of charged particles and satellites can result in partial or total loss, leading to the satellite's inoperable. Charging on the satellite is the dominant cause of malfunctions and failures of satellite components. Damage mitigation can be done through a series of experiments in which materials and satellite components are bombarded with high- energy electrons. In this study, electrons are generated and accelerated using an electron beam machine (EBM). The test material and electronic components are placed beneath the EBM's beam window, where a 300 keV electron beam bombards them uniformly. The experimental results show that the electron beam can penetrate all the test materials used as shields within 20 s, and the absorbance levels range between 67% and 98%. The penetration depths span between 0.16 and 0.46 mm and agree with the Kanaya-Okayama calculation. The values of stopping power for most materials show the ineffectiveness of materials for shielding. However, increasing the thickness, such as in the MLI case, can reduce the level of electron radiation absorption. This experiment provides recommendations on the selection of materials used for equatorial satellite shielding, particularly the next Indonesian LEO equatorial satellite, both in terms of material and size. [ABSTRACT FROM AUTHOR]

Serntinel 2A provide Normalized Difference Vegetation Index (NDVI) to be used as an estimate of soil fertility, plant varieties and productivity. The weakness of satellite data is that the data obtained is often inaccurate due to cloud cover, especially in tropical countries with high rainfall such as Indonesia. The use of unmanned aerial vehicle (UAV) as an alternative data have limitation as it captured Red Green Blue (RGB) imagery. The research was conducted from July to September 2020 at Pasir Kaliki Village, District of Rawamerta, Karawang Regency, West Java province. The study has discovered that NDVI showed higher number in result of vegetation index compared to Normalized Green-Red Difference Index (NGRDI) with correlation coefficient is 0.94. The regression model resulted as y=4.78x+0.38 and MAPE value expresses as 26.74%, where the regression model with Pearson’s correlation coefficient value is 0.88. A qualitative assessment using statistical data and a spatial assessment using sampled data from the rice vegetation map reveal a high mapping accuracy with the corresponding R2 being as high as 0.74; however, the mapped rice vegetation accuracy might influenced by other physical factors such as water reflectant, sunlight and the RGB camera limitation itself. Nonetheless, the highest values of NGRDI only reach 0.2 while NDVI can attain at 0.9 at the peak of vegetative phase of rice growth stage. This means that Green Band have limitation in detecting vegetation index. In relation to the different approaches performed, it is noted that the average trend line on both NDVI and NGRDI shown the similarity tendency in all growth stage. [ABSTRACT FROM AUTHOR]

The estimation of leaf area index (LAI) becomes important as LAI is one of the parameters in analyzing the crop growth model. Crop growth has different characteristics and it’s strongly influenced by environmental conditions and factors. The growth tends to occur in a short period and covers a large area. Therefore, an approach to analyzing the pattern of changes in crop growth based on LAI spatially is needed. Remote sensing offers an effective and efficient approach to monitoring crop growth characteristics, which can be done in a time series with a wide area coverage by detecting and monitoring the physical characteristics of the crop. The most famous and commonly used parameters to estimate LAI are vegetation indices which are usually calculated based on the ratio of the red and NIR wavelength, known as a spectral signature. The objectives of the research are to examine the Spatio-temporal correlation between LAI of three rice cultivars Sentinel-2A based vegetation indices and to select the most optimum vegetation index in estimating LAI. The field experiment was set up comprising 81 plots, each had a size of 10m x 10 m to resemble a pixel of Sentinel-2A imagery. The results of the analysis show that the vegetation index has a strong correlation with LAI. The Comparison of the four calculated vegetation indices in estimating LAI was performed using a linear regression model and followed by comparing R-squared, RMSE, and Correctness. In general, the EVI2 vegetation index provides the most optimum representation in capturing crop growth patterns based on LAI compared to NDVI, ARVI and SAVI vegetation indices calculated from Sentinel-2A satellite imagery indicated by the better-validated model with the result of RMSE value are 1.12 on V1, 1.11 on V2 and 0.70 on V3. The result of EVI2 Correctness also showed the highest value compared to the other vegetation indices with values of more than 60%, 64.15% on V1, 65.51% on V2, and 78.69% on V3. Further analysis by separating two growth stages could overcome the bias that appears in the LAI data for one life of the crop cycle which is indicated by the decrease of RMSE value on each cultivar planted for both vegetative and generative phases, except for cultivar V3 for the generative phase. The separation of data into two growth stages also increase the percentage value of correctness reaching a number above 60% and there was a value that reached 87%. [ABSTRACT FROM AUTHOR]